In supervised tracking methods, box-regression branch has been demonstrated to be effective to capture the objects with large scale variation along temporal dimension [1]}, [2]}.
However, in existing unsupervised methods, this branch is always absent [3]}, [4]}, [5]}. Recently, USOT [6]} introduced a box-regression head, but the tracker is initially trained with template-search pairs from single frames, followed by cycle memory training to enhance the robustness of classification branch, whereas the box-regression branch is trained with spatial supervision alone.
In this paper, we aim to train a better tracker by learning temporal correspondence both on the classification branch and on the regression branch.
However, we identify there are three critical challenges.
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